{"id":"W4297982465","doi":"10.3390/ijerph191912398","title":"The Comprehensive Alcohol Advertising Ban in Lithuania: A Case Study of Social Media","year":2022,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Centre for Addiction and Mental Health","funders":"National Institute on Alcohol Abuse and Alcoholism; National Institutes of Health","keywords":"Social media; Advertising; Alcohol advertising; Environmental health; Media coverage; Business; Suicide prevention; Poison control; Political science; Medicine; Sociology; Media studies; Law","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005037285,0.0003879622,0.0003177862,0.001809936,0.004133014,0.001824948,0.0006852235,0.001386902,0.002122215],"category_scores_gemma":[0.001268623,0.0003765934,0.0003100614,0.001583944,0.00142566,0.0007340781,0.001712329,0.0008077614,0.0002684022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001798012,"about_ca_system_score_gemma":0.001526437,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0242753,"about_ca_topic_score_gemma":0.03833975,"domain_scores_codex":[0.9993663,0.0001781238,0.00004029192,0.0000509647,0.0001050385,0.0002592287],"domain_scores_gemma":[0.9991581,0.0002116408,0.0002953363,0.00005422204,0.00008030405,0.0002003769],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002332744,0.001250869,0.4840721,0.0003636105,0.00007732735,0.333013,0.136151,0.0002151767,0.002769578,0.003349592,0.002417019,0.03608734],"study_design_scores_gemma":[0.0000217178,0.00062433,0.5379521,0.0003757551,0.0001025741,0.1291467,0.3077514,0.001131476,0.002172093,0.0006899195,0.0199577,0.00007415862],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977577,0.0002309007,0.00006568732,0.0002864369,0.000007979597,0.00002168368,0.00002533368,0.000003773258,0.001600484],"genre_scores_gemma":[0.9975396,0.0005852602,0.000144245,0.0001600156,0.00001868592,0.0000139044,0.00002800804,0.000005654566,0.001504482],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0242753,"threshold_uncertainty_score":0.04826802,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1165399424961433,"score_gpt":0.4008850349093324,"score_spread":0.2843450924131892,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}